As 3D scanning devices and depth sensors advance, dynamic point clouds have
attracted increasing attention as a format for 3D objects in motion, with
applications in various fields such as immersive telepresence, navigation for
autonomous driving and gaming. Nevertheless, the tremendous amount of data in
dynamic point clouds significantly burden transmission and storage. To this
end, we propose a complete compression framework for attributes of 3D dynamic
point clouds, focusing on optimal inter-coding. Firstly, we derive the optimal
inter-prediction and predictive transform coding assuming the Gaussian Markov
Random Field model with respect to a spatio-temporal graph underlying the
attributes of dynamic point clouds. The optimal predictive transform proves to
be the Generalized Graph Fourier Transform in terms of spatio-temporal
decorrelation. Secondly, we propose refined motion estimation via efficient
registration prior to inter-prediction, which searches the temporal
correspondence between adjacent frames of irregular point clouds. Finally, we
present a complete framework based on the optimal inter-coding and our
previously proposed intra-coding, where we determine the optimal coding mode
from rate-distortion optimization with the proposed offline-trained $\lambda$-Q
model. Experimental results show that we achieve around 17% bit rate reduction
on average over competitive dynamic point cloud compression methods.

本文提出了一种用于三维动态点云属性的完整压缩框架，主要关注于优化交叉编码。通过使用先前提出的内部编码和离线训练的 λ-Q 模型，我们从速率失真优化中确定最佳编码模式。实验结果表明，与竞争性的动态点云压缩方法相比，平均比特率降低了约 17％。